U.S. patent application number 12/968455 was filed with the patent office on 2012-06-21 for system and method for evaluating a level of knowledge of a healthcare individual.
Invention is credited to Peter HURWITZ, John Lapolla.
Application Number | 20120156664 12/968455 |
Document ID | / |
Family ID | 46234880 |
Filed Date | 2012-06-21 |
United States Patent
Application |
20120156664 |
Kind Code |
A1 |
HURWITZ; Peter ; et
al. |
June 21, 2012 |
SYSTEM AND METHOD FOR EVALUATING A LEVEL OF KNOWLEDGE OF A
HEALTHCARE INDIVIDUAL
Abstract
The invention relates to a system and method for evaluating a
level of knowledge of a healthcare individual by providing a
computer; software executing on the computer for prompting a
healthcare individual to answer a first question from a first
plurality of questions related to diagnosis or treatment of a
health related ailment; software executing on the computer for
automatically determining a degree of correctness of an answer to
the first question by comparing the answer to a reference; software
for automatically selecting a second question from a second
plurality of questions based upon the degree of correctness;
software for automatically generating a score based upon the degree
of correctness; and, based upon a cumulation of scores, software
for automatically determining whether or not the healthcare
individual is permitted to progress to a next level for diagnosis
or treatment based upon the score.
Inventors: |
HURWITZ; Peter; (New York,
NY) ; Lapolla; John; (New York, NY) |
Family ID: |
46234880 |
Appl. No.: |
12/968455 |
Filed: |
December 15, 2010 |
Current U.S.
Class: |
434/262 |
Current CPC
Class: |
G09B 23/28 20130101;
G09B 7/02 20130101 |
Class at
Publication: |
434/262 |
International
Class: |
G09B 23/28 20060101
G09B023/28 |
Claims
1. A system for evaluating a level of knowledge of a healthcare
individual, comprising: providing a computer; software executing on
said computer for prompting a healthcare individual to answer a
first question from a first plurality of questions related to
diagnosis or treatment of a health related ailment; software
executing on said computer for automatically determining a degree
of correctness of an answer to said first question by comparing the
answer to a reference; software executing on said computer for
automatically selecting a second question from a second plurality
of questions based upon the degree of correctness; software
executing on said computer for automatically generating a score
based upon the degree of correctness; and software executing on
said computer for, based upon a cumulation of scores, automatically
determining whether or not the healthcare individual is permitted
to progress to a next level for diagnosis or treatment based upon
the score.
2. The system according to claim 1, further comprising software
executing on said computer for selecting a reference selected from
the group consisting a panel of experts, a medical journal, a
medical profession, a medical association, and combinations
thereof.
3. The system according to claim 1, further comprising software
executing on said computer for automatically providing diagnosis or
treatment information based upon the score.
4. The system according to claim 1, further comprising software
executing on said computer for automatically providing education to
the healthcare individual based upon the score
5. The system according to claim 1, further comprising software
executing on said computer for randomizing said first and second
plurality of questions from a database of questions related to
diagnosis and treatment of various ailments.
6. The system according to claim 1, further comprising software
executing on said computer for certifying the healthcare individual
upon completion of the education.
7. The system according to claim 1, further comprising software
executing on said computer for using qualitative reasoning when
determining the degree of correctness.
8. The system according to claim 7, further comprising software
executing on said computer for using quantitative reasoning when
determining the degree of correctness.
9. The system according to claim 1, further comprising software
executing on said computer for extracting empirical data when
determining the degree of correctness.
10. The system according to claim 1, further comprising software
for extracting information from media for formulating said at least
one question.
11. A system for evaluating a level of knowledge of a healthcare
individual, comprising: providing a computer; software executing on
said computer for providing a first question related to diagnosis
or treatment of a health related ailment, wherein said first
question is for prompting the healthcare individual to answer;
software executing on said computer for, based upon a first answer
to said first question from the healthcare individual,
automatically determining a degree of correctness of the first
answer; software executing on said computer for, based upon the
degree of correctness of the first answer, automatically generating
a first score; software executing on said computer for, based upon
the degree of correctness of the first answer, automatically
providing a next question related to diagnosis or treatment of the
health related ailment; and software executing on said computer
for, based upon a cumulation of scores, automatically determining
whether or not the healthcare individual is permitted to progress
to a next level for diagnosis or treatment based upon the
score.
12. The system according to claim 11, further comprising software
executing on said computer for, based upon a cumulation of scores,
automatically providing education to the healthcare individual.
13. The system according to claim 11, further comprising software
executing on said computer for, based upon completion of the
education, certifying the healthcare individual
14. The system according to claim 11, further comprising software
executing on said computer for, based upon a degree of correctness
of a first answer, automatically generating a score in real
time.
15. The system according to claim 11, further comprising software
executing on said computer for using qualitative reasoning when
determining the degree of correctness.
16. The system according to claim 15, further comprising software
executing on said computer for extracting empirical data when
determining the degree of correctness.
17. A method for evaluating a level of knowledge of a healthcare
individual, comprising the steps of: providing a computer;
providing a first question related to diagnosis or treatment of a
health related ailment, wherein said first question is for
prompting the healthcare individual to answer; based upon a first
answer to said first question from the healthcare individual,
automatically determining a degree of correctness of the first
answer; based upon the degree of correctness of the first answer,
automatically generating a first score; and based upon the degree
of correctness of the first answer, automatically providing a next
question related to diagnosis or treatment of the health related
ailment; and based upon a cumulation of scores, automatically
determining whether or not the healthcare individual is permitted
to progress to a next level for diagnosis or treatment based upon
the score.
18. The method according to claim 17, further comprising the step
of, based upon a cumulation of scores, automatically providing
education to the healthcare individual.
19. The method according to claim 17, further comprising the step
of using qualitative reasoning when determining the degree of
correctness.
20. The method according to claim 17, further comprising the step
of extracting empirical data when determining the degree of
correctness.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application No. 61/266,055 filed on Dec. 2, 2009. The contents of
the above-identified Application is relied upon and incorporated
herein by reference in its entirety.
RESERVATION OF COPYRIGHT
[0002] A portion of the disclosure of this patent document contains
material to which a claim of copyright protection is made. The
copyright owner has no objection to the facsimile reproduction by
anyone of the patent document or the patent disclosure as it
appears in the Patent and Trademark Office patent file or records,
but reserves all other rights whatsoever.
FIELD OF INVENTION
[0003] The invention relates to a system and method of
automatically determining a level of knowledge of a healthcare
individual and, depending upon the determined level, providing
learning tools to the healthcare individual.
BACKGROUND OF INVENTION
[0004] The healthcare industry typically suffers from information
flow and workflow fragmentation. Traditionally, information was
usually exchanged among various parties involved in healthcare,
such as physicians, hospitals, insurers, students, medical staff,
laboratories, employers, and others, using paper-based methods. As
is well know in the art, such methods are often labor-intensive,
inefficient, and error prone. Thus, some efforts were undertaken to
improve the healthcare industry through the use of electronic
information networks integrating healthcare participants.
[0005] However, electronic information may introduce other
difficulties, such as implementation of an electronic information
network that is reliable and consistent with traditional medical
practices. Another difficulty is ensuring such electronic
information is helpful to each healthcare individual ("HCI"),
especially when one HCI may have a level of knowledge that is
different from a next HCI.
[0006] In traditional information exchange prior to implementation
of electronic information networks, there was often more human
interaction on a personal or individual basis, wherein the human
interactions would reveal a level of knowledge among various HCIs
so that information exchanged thereafter would correspond to the
level of knowledge of each HCI.
[0007] With the advent of electronic information, there is
typically less human interaction and, as a result, this may cause
less individualized attention or information that is the same or
similar and that do not take into account varying levels of
knowledge among the HCIs. This problem may be exacerbated when the
information is for diagnosis, treatment, education, training, and
the like, wherein providing information without accounting for
varying levels of knowledge can negatively affect patients.
[0008] What is desired, therefore, is an invention that identifies
gaps in knowledge. Another desire is a system and method that
identifies gaps in performance behaviors. A further desire is a
system and method of determining whether or not a healthcare
individual is competent to perform a particular service. Yet
another desire is a system and method that automatically poses
questions to the healthcare individual to determine the competency
and, based upon answers to said questions, automatically assigning
a score to the healthcare individual. Still another desire is a
system and method that, based upon the score, automatically permits
the individual to progress to a next level of information or
provides learning tools to the healthcare individual. Another
desire is a system and method that measures each answer against
findings from an expert panel and, to the extent that their answers
fall short of standards and best practices, the healthcare
individual will receive educational activities until such time as
they can demonstrate an advance in knowledge.
SUMMARY OF INVENTION
[0009] It is therefore an object of the invention to provide a
system for evaluating a level of knowledge of a healthcare
individual.
[0010] Another object is to provide a system that identifies gaps
in knowledge of a HCI.
[0011] A further object is a system that automatically determines
the level of competency of a HCI by determining a degree of
correctness or score to each answer or, depending upon the area of
health, each group of questions provided by the HCI.
[0012] Another object is a system that, based upon the degree of
correctness or score, automatically determines whether or not the
HCI is competent enough to progress to a next level of questions,
diagnosis, or treatment.
[0013] Yet another object is a system that, in the event the degree
of correctness or score is not sufficient, automatically sends
learning tools or education to the HCI.
[0014] These and other objects are achieved by providing a
computer; software executing on the computer for prompting a
healthcare individual to answer a first question from a first
plurality of questions related to diagnosis or treatment of a
health related ailment; software executing on the computer for
automatically determining a degree of correctness of an answer to
the first question by comparing the answer to a reference; software
for automatically selecting a second question from a second
plurality of questions based upon the degree of correctness;
software for automatically generating a score based upon the degree
of correctness; and, based upon a cumulation of scores, software
for automatically determining whether or not the healthcare
individual is permitted to progress to a next level for diagnosis
or treatment based upon the score.
[0015] In some embodiments, the system further includes software
for providing a first question related to diagnosis or treatment of
a health related ailment, wherein the first question is for
prompting the healthcare individual to answer. In another
embodiment, software selects a reference from the group consisting
a panel of experts, a medical journal, a medical profession, a
medical association, and combinations thereof.
[0016] In another embodiment, the system includes software
executing on the computer for automatically providing diagnosis or
treatment information based upon the score.
[0017] In further embodiments, the system includes software for
automatically providing education to the healthcare individual
based upon the score.
[0018] In an optional embodiment, the system has software for
randomizing the first and second plurality of questions from a
database of questions related to diagnosis and treatment of various
ailments.
[0019] In other embodiments, the system includes software executing
on the computer for certifying the healthcare individual upon
completion of the education.
[0020] In some embodiments, the system has software executing on
the computer for using qualitative reasoning when determining the
degree of correctness. In some of these embodiments, the system
includes software executing on the computer for using quantitative
reasoning when determining the degree of correctness.
[0021] In a further embodiment, the system includes software
executing on the computer for extracting empirical data when
determining the degree of correctness.
[0022] In another embodiment, based upon completion of the
education, software certifies the healthcare individual.
[0023] In some embodiments, the system has software executing on
the computer for extracting information from media for formulating
said at least one question.
[0024] In another aspect of the invention, a method for evaluating
a level of knowledge of a healthcare individual includes the steps
of providing a computer; providing a first question related to
diagnosis or treatment of a health related ailment, wherein the
first question is for prompting the healthcare individual to
answer; based upon a first answer to the first question from the
healthcare individual, automatically determining a degree of
correctness of the first answer; based upon the degree of
correctness of the first answer, automatically generating a first
score; based upon the degree of correctness of the first answer,
automatically providing a next question related to diagnosis or
treatment of the health related ailment; and based upon a
cumulation of scores, automatically determining whether or not the
healthcare individual is permitted to progress to a next level for
diagnosis or treatment based upon the score.
[0025] In one embodiment, the method, based upon a cumulation of
scores, automatically provides education to the healthcare
individual.
[0026] In another embodiment, the method includes the step of using
qualitative reasoning when determining the degree of
correctness.
[0027] In an optional embodiment, the method extracts empirical
data when determining the degree of correctness.
BRIEF DESCRIPTION OF FIGURES
[0028] FIG. 1 depicts the system in accordance with the
invention.
[0029] FIG. 2 more particularly depicts the user preferences shown
in FIG. 1.
[0030] FIG. 3 more particularly depicts the baseline knowledge and
scoring shown in FIG. 1.
[0031] FIG. 4 more particularly depicts the case study shown in
FIG. 1.
[0032] FIG. 5 more particularly depicts alternatives to the case
study shown in FIG. 1.
[0033] FIG. 6 depicts empirical data and search terms as applied to
the system shown in FIG. 1.
[0034] FIG. 7 depicts the software for the system shown in FIG.
1.
[0035] FIG. 8 more particularly depicts the software for the
scoring shown in FIG. 1.
[0036] FIG. 9 depicts a method of providing the system shown in
FIG. 1.
DETAILED DESCRIPTION OF DRAWINGS
[0037] FIG. 1 depicts system 1 for evaluating a level of knowledge
of a healthcare individual. System 1 provides user or healthcare
individual ("HCI") 2 with heuristic learning environment 14,
wherein HCI 2 logs into a computer as registered user 4 or new user
6. If logging on as new user 6, HCI 2 needs to enter user
preferences 10, which are saved in database 12 and are retrieved at
a later login as registered user 4.
[0038] Once logged into system 1. HCI 2 is greeted with learning
environment 14 and prompted to select a therapeutic area of from a
selection of therapeutic areas 1-n. As shown, there are three
therapeutic areas but any number n of areas may be presented,
wherein HCI would select which one he/she wishes to enter.
Therapeutic areas 1-n are a description of the medical field HCI
wishes to enter. For example, skin disorders, cardiology,
neurology, traumatic spinal injuries are all examples of
therapeutic areas.
[0039] After entering the selected therapeutic area, baseline
knowledge 20 is established, which entails a series of questions to
establish a baseline of the HCI from which said HCI will be scored.
Baseline questions will be related to the selected therapeutic area
to get an understanding of knowledge HCI has in said area. This is
different from information retrieved when HCI is a registered user
4 or information stored in user preferences 10.
[0040] For example, a cardiologist seeking to understand spinal
injuries more will have a different baseline than a spinal injury
specialist. Baseline knowledge 20 ascertains this difference in
knowledge via questions and answers and is more particularly
described below under the description for FIG. 3.
[0041] After baseline knowledge 20 is established, HCI is
determined to be a generalist in which case the HCI would be placed
in generalist track 24 or HCI is determined to be a specialist in
which case the HCI would be placed in specialist tract 28.
[0042] Subsequent to establishment of the foregoing, HCI would
choose which a further area of study within the selected
therapeutic area. For example, if the therapeutic area is
cardiology, further areas of study may include open heart surgery,
diseases of the heart, causes of a ruptured aorta, and the like.
This further study is either case study 40 (see FIG. 4), Risk
Evaluation and Mitigation Strategies ("REMS") 58 (see FIG. 5), or
Prescribing Information ("PI") 60 (see FIG. 5).
[0043] As shown in FIG. 2, user preferences is more particularly
depicted, wherein information of HCI are submitted to database 180
after logging on. Examples of the HCI information submitted include
name 41, location or address 42, specialty 43 such as cardiologist,
area of interests 44 such as surgery, and the type of device from
which HCI will submit answers or receive questions from system 1,
including a mobile device 45. GPS location 46, internet provider
47, and the like.
[0044] FIG. 3 more particularly depicts baseline knowledge 20 in
accordance with the invention. As shown, baseline knowledge or
baseline questions 20 include a series of questions 1-n and
multiple choice answers to each question A1, A2, A3 along with the
ability for HCI to enter text to supplement an answer.
[0045] The purpose of baseline knowledge 10 is for establishing a
starting point for HCI from which he/she will be measured.
Different HCIs have different starting points because they have
varying levels of knowledge. Otherwise, it would be unfair to score
or assess the degree of correctness for all HCIs from a single
starting point, where doing so may lead to inaccurate scores.
[0046] Also shown in FIG. 3 is software 30 for scoring an answer or
determining a degree of correctness of an answer. This software is
used in at least two locations in system 1, after baseline
knowledge 10 and after answers are given by HCI within case study
40.
[0047] As shown in FIG. 3, answers to baseline questions 10 are
compared with expert panel 32, quantitative scoring 34, and
qualitative scoring 38. Software 30 for scoring is needed such that
an initial assessment may be made of the HCI so that a baseline may
be established.
[0048] FIG. 4 depicts case study 40 in greater detail. After
baseline knowledge 10 is established. HCI is given a plurality of
further areas of study, where case study 40 is one of these further
areas of study. Case study 40 includes recognition module 42,
assessment module 44, differential diagnosis module 46, and
therapeutic management module 48. HCI selects which module to
receive information. For example, if HCI wishes to receive
diagnosis information, he/she would select differential diagnosis
module 46.
[0049] Once selected, system 1 would begin with a first chapter 66
with diagnosis information (following example above) followed by
multimedia 76, which may be video 77, slides 78, and/or text 79.
Upon the conclusion of the multimedia and first chapter, a first
question 86 is presented. HCI must provide answer 89 to first
question 86. Upon receipt of answer 89, software 30 for scoring
answer 89 determines a degree of correctness of answer 89 and
assigns a score to answer 89.
[0050] In some embodiments, several questions need to be answered,
in which case questions 1-n are presented and each are answered
before software 30 scores the answers and determines a degree of
correctness. The software for scoring and determining the degree of
correctness is more particularly described in FIG. 7.
[0051] In an optional embodiment, after the degree of correctness,
as well as determining if correct 96 or incorrect 98, has been
determined, it becomes part of baseline knowledge 20 for the
particular HCI.
[0052] In other embodiments, once the degree of correctness has
been determined, and assuming the HCI scores above the required
threshold for the answer or answers, the HCI progresses to the next
level, in this case chapter two and followed by multimedia and
additional questions. This cycle continues until HCI completes all
chapters or until HCI demonstrates he/she scores below the required
threshold, in which case HCI is sent education.
[0053] In one embodiment, HCI needs to finish continuing education
requirements. In this case, after successfully progressing through
all levels, a certification 145 is given certifying completion.
[0054] In an embodiment for REMS or PI, collectively shown in FIG.
5 as reference 56, the same procedure is followed as case study 40
in FIG. 4. FIG. 6 depicts external database 181 to which HCI
uploaded empirical data, such as work HCI (assuming for this
example HCI is a physician) performed in the field like hospital
charts, dosing information to patients, recovery time for patients
in view of HCI's care, and other data typically found from working
as a physician.
[0055] In some embodiments, baseline knowledge 20 is based upon the
empirical data in addition to the above mentioned questions and
answers during baseline questions. Differences between HCI's
empirical data and suggestions from an expert panel stored on
database 181 assist in determining a baseline for HCI. This
difference or gap 15 is then stored and made a part of baseline
knowledge 10. Further use of empirical data is further discussed
below under FIG. 8.
[0056] As shown in FIG. 7, system 1 includes providing 130 a
computer, software 106 executing on said computer for prompting a
healthcare individual to answer a first question from a first
plurality of questions related to diagnosis or treatment of a
health related ailment, software 112 executing on said computer for
automatically determining a degree of correctness of an answer to
said first question by comparing the answer to a reference,
software 116 executing on said computer for automatically selecting
a second question from a second plurality of questions based upon
the degree of correctness, software 120 executing on said computer
for automatically generating a score based upon the degree of
correctness, and software 124 executing on said computer for, based
upon a cumulation of scores, automatically determining whether or
not the healthcare individual is permitted to progress to a next
level for diagnosis or treatment based upon the score.
[0057] In some embodiments, reference 188 is selected from the
group consisting a panel of experts, a medical journal, a medical
profession, a medical association, and combinations thereof. In a
further embodiment, reference 188 is any standard accepted by
medical professions in the field, any standard accepted by
academics, and combinations thereof.
[0058] Database 180 stores reference 188, first plurality 182 of
questions, and second plurality 184 of questions.
[0059] Software 112 for automatically determining a degree of
correctness of an answer does so after each answer is given. Unlike
traditional tests or questioning, which may have assessed
performance of a HCI after completing of all questions and answers,
software 112 determines correctness after every question.
[0060] In some cases, software 112 does so in real time, which
means software 116 for automatically selecting a second question
bases its selection upon the degree of correctness determined
immediately prior to software 116 automatically selecting the
second question and after software 106 prompts HCI to answer the
question. In this manner, software 112 determines the degree of
correctness immediately and in real time so that the second
question is appropriate for the level of knowledge of HCI 2.
[0061] As shown, software 112 determines the degree of correctness
by comparing the answer with reference 188. In some embodiments,
software 112 does not merely compare an answer from HCI with
reference 188 and determines it is correct or incorrect, but
instead determines to what degree the answer is correct or
incorrect. In other words, software 112 determines how far off
HCI's answer is to reference 188.
[0062] For example, if the question is directed to proper treatment
of pain in the ears, and there are multiple answers such as
applying a cold pack, taking medication, or taking a look into the
ear canal for signs of an ear infection, software 112 may determine
the last choice would be given a higher degree of correctness or is
more correct than the second choice, which is also correct but to a
lesser extent of correctness as the last choice. The first choice
would indicate an incorrect answer or a least correct answer among
the three choices. Scoring the HCI would likewise vary in
corresponding fashion where the second choice would be scored
higher than the first choice and the third choice would be scored
higher than the second choice.
[0063] In another embodiment, software 112 makes this determination
after several answers instead of after each answer, depending upon
therapeutic area 16-18, reference 188, and other factors that may
require answers to several questions in order to assess a degree of
correctness.
[0064] For example, if the therapeutic area of Alzheimer is complex
and often difficult to diagnose properly, several questions
directed to behavior over time may be required, according to an
expert panel or reference 188. In this case, several answers are
needed before a degree of correctness may be assessed for a
HCI.
[0065] In another example, if the therapeutic area is skin lesions,
the injuries are easily seen and thus a degree of correctness and
score may be generated after a single question.
[0066] Hence, the number of questions and answers needed before a
degree of correctness is determined depends upon the therapeutic
area, expert panel, reference 188, and generally the science
involved, such as its complexity and the general knowledge among
those in the field of the chosen therapeutic area.
[0067] In some embodiments, and as shown in FIG. 8, software 112
for automatically determining a degree of correctness includes
software 144 for using qualitative reasoning when determining the
degree of correctness. Qualitative reasoning and its effect on
determining correctness is more particularly defined below.
[0068] In another embodiment, software 112 for automatically
determining a degree of correctness further includes software 146
for using quantitative reasoning when determining the degree of
correctness. Quantitative reasoning and its effect on determining
correctness is more particularly defined below.
[0069] In a further embodiment, software 112 for automatically
determining a degree of correctness also includes software 148 for
extracting empirical data 149 from database 180 when determining
the degree of correctness.
[0070] Empirical data 149 is data loaded onto database 180 by HCI,
typically during user preferences 10 or baseline questioning 20,
which relates to the HCI's practices in the field, such as past
diagnosis, dosing, treatment, and the like. For example, if HCI is
a hospital physician, past medical charts are loaded to database
180, where the medical charts may relate to previous notes,
prescriptions, treatment, and patient's length of stay and response
to the prescriptions and treatments.
[0071] In some embodiments, in addition to the answers given by
HCI, empirical data 149 is reviewed by software 112 for determining
the degree of correctness. Hence, qualitative and/or quantitative
reasoning are applied by software 112 not just to the answers to
questions, but also to empirical data.
[0072] Subsequent to determining a degree of correctness, software
120 for automatically generating a score provides a score for each
answer or, in the event several answers are needed before an
assessment is made, provides a score for several answers.
[0073] Once a score is determined, software 124 for automatically
determining whether or not HCI progresses to a next level either
permits HCI access to the next level or sends education 139 to
HCI.
[0074] If the score meets a threshold amount, system 1 includes
software 138 for automatically providing the next level of
diagnosis or treatment information based upon the score.
[0075] In some cases, next level 137 includes a next question or
next set of questions. In other cases, next level 137 includes
diagnosis or treatment information.
[0076] The threshold amount is stored in database 180 and is
predetermined and dependent upon therapeutic area 16-18, reference
188, and combinations thereof.
[0077] In the event score does not meet threshold amount, system
includes software 140 for automatically providing education to the
healthcare individual based upon the score. In some embodiments,
software 140 automatically provides education based upon a
cumulation of scores, such as the end of questioning or when enough
scores from individual answers are tabulated and a conclusion is
drawn, when compared to reference 188, that the HCI needs
learning.
[0078] Education 139 is sent because HCI demonstrated a level of
knowledge that is below reference 188. Therefore, education 139 is
sent to HCI to assist in raising the level of knowledge. Education
139 is sent in the form of audio, text, video, and combinations
thereof.
[0079] In another embodiment, upon completion of education 139 or
evidence that education was completed 143, such as a final written
document, system 1 includes software 142 for certifying the HCI. In
some of these embodiments, certification 145 is a document very
much a continuation education credit or other certificate issued by
an accredited university or academic institution.
[0080] The certification process for non-accredited programs may
include the programs such as a class-wide opioid Risk Evaluation
and Mitigation Strategy (REMS) supported by a Pharmaceutical or
other Healthcare-related Company. This certification process would
include necessary requirements to satisfy the funding Company
and/or FDA requirements and be intended for prescribers,
pharmacists and other HCIs.
[0081] In order to meet the requirements of the restrictive
post-marketing control system under the new FDA Amendments Act
authority, the FDA has suggested an opioid class REMS for the
healthcare marketers and products in the long-acting and
extended-release opioid category in order to prove competency and
minimal demonstration of understanding of product or procedure
risk. This opioid-class REMS will increase the level of safety and
lower the risk associated with opioid drugs, with the hope of
positively affecting patient outcomes.
[0082] The certification process will recognize those healthcare
providers, through a registry, who have completed and/or
successfully passed the necessary training, education, and who have
received the minimal requirements needed, as directed by the FDA or
sponsoring organization, in order to prescribe a medication or
conduct a specific medical procedure.
[0083] Those healthcare providers that complete the necessary
training and achieved a specified level of certification may be
enrolled in a database or registry that could be monitored by the
sponsoring or an outside auditing entity. Those healthcare
providers that do not take part nor have completed a specified and
required certification program may be limited in their prescribing
authority or denied authority to perform certain medical
procedures.
[0084] In view of the foregoing, the score generated after each
question gives diagnosis or treatment information tailored to the
individual HCI based on the HCI's level of knowledge. Similarly,
the education given to the HCI is tailored to the individual HCI
based on the HCIO's level of knowledge.
[0085] In an optional embodiment, system 1 includes software 152
executing on said computer for randomizing the first plurality of
questions from a database of questions related to diagnosis and
treatment of various ailments. This randomizing occurs before
software 162 provides a first question to the HCI, wherein first
question 131 is related to diagnosis or treatment of a health
related ailment, wherein said first question is for prompting the
healthcare individual to answer.
[0086] In some of these embodiments, system also includes software
156 executing on said computer for randomizing the second plurality
of questions. This randomizing occurs before software 166 executing
on said computer for, based upon the degree of correctness of the
first answer 133, automatically providing a next question to the
HCI, wherein next question 135 is related to diagnosis or treatment
of the health related ailment. Randomizing makes the types of
questions posed less predictable and harder for a HCI to memorize
or anticipate a correct answer.
[0087] In a further embodiment, the first question is formulated by
software that automatically extracts information from media, such
as news, academics, and journals. In these embodiments, in addition
to empirical data 149, software would automatically read and
extract, based on key word searching, relevant information that is
publicly available, such as treatises, news, articles,
dissertations, and the like.
[0088] In some of these embodiments, system 1 also includes
software for extracting information from media for determining the
degree of correctness of the answer. In these embodiments, very
much like empirical data 149 is used for determining the degree of
correctness, software 112 uses the extracted information above in
determining the degree of correctness.
[0089] In one aspect of the invention, and as shown in FIG. 9,
method 300 is provided for evaluating a level of knowledge of a
healthcare individual, including the steps of providing 302 a
computer; providing 304 a first question related to diagnosis or
treatment of a health related ailment, wherein said first question
is for prompting the healthcare individual to answer; and, based
upon a first answer to said first question from the healthcare
individual, automatically determining 308 a degree of correctness
of the first answer.
[0090] Method 300 also includes, based upon the degree of
correctness of the first answer, automatically generating 310 a
first score and, based upon the degree of correctness of the first
answer, automatically providing 314 a next question related to
diagnosis or treatment of the health related ailment. Method 300
also provides, based upon a cumulation of scores, automatically
determining 318 whether or not the healthcare individual is
permitted to progress to a next level for diagnosis or treatment
based upon the score.
[0091] If method 300 determines the HCI is permitted to progress to
a next level, method 300 includes the step of providing 328 a next
level. If method 300 determines the HCI is not permitted to
progress to a next level, method 300 includes the step of
automatically providing 322 education and, upon completion of the
education, providing 324 certification.
[0092] In some embodiments, method 300 includes using 322
qualitative reasoning when determining the degree of correctness.
In other embodiments, method 300 includes using 324 quantitative
reasoning when determining the degree of correctness. In a further
embodiment, method 300 includes extracting 326 empirical data when
determining the degree of correctness.
[0093] The following describes the purpose and operation of the
invention in greater detail along with examples of the
invention.
[0094] Once the healthcare individual (HCI), which may be a
practitioner, has registered with a user name and password and
logged in with a self-identified specialty, the user may select
from a series of educational offerings. Each activity is developed
to reflect core competencies for generalists and specialists alike,
as determined by an expert panel. The user experience is seamless,
providing in real time multimedia education based on quantitative
and qualitative assessment of the end user's responses.
[0095] Fundamental to the proposed invention is a scoring system
that automatically evaluates HCI knowledge, practice behaviors,
attitudes, and biases. Since much of medicine today is predicated
on subjective interpretation of available evidence and, when the
evidence is lacking, on clinical experience, the scoring system
helps bridge the inevitable gaps in HCI practices with expert
consensus. More precisely, the scoring system reflects expert
empirical observations and interpretation of the strengths and
limitations of current evidence-based guidelines. The invention
utilizes questionnaires formulated by an expert panel of HCIs,
educators, and statisticians that, on a question by question basis,
reveal end user deficits in knowledge and evidence-based best
practices.
[0096] The granularity and level of rigor of each question can be
tailored to the end user's specialty, subspecialty, and patient
population. Further, the questionnaire constitutes an elaborate
decision tree: the end user's response to each question begets a
multimedia presentation--in text, audio, and/or video
streaming--explicating the merits or weaknesses of the selection,
as defined by the expert panel. If the end user should fail to
answer a question correctly, the logic of the code permits the
seamless, real-time selection of follow-on educational activities
until the end user is capable of answering similar questions
correctly, demonstrating comprehension and core competency in
particular subject matter(s). The questionnaire is embedded in the
code and the educational activities are selected as a function of
each respondent's score.
[0097] As discussed herein, the invention reflects current needs
for education that marries evidence based practice with practice
based evidence--ie, education that targets the inherently ambiguous
junction where evidence from well designed studies ends and
clinical judgment begins. Here, clinicians necessarily apply the
evidence insofar as possible and exercise their discretion, relying
on experience and guidance from local, regional, and national
thought leaders (See below for select examples).
[0098] Notably, evidence based recommendations are predicated on
studies of populations with rigorous inclusion and exclusion
criteria; therefore, these and related practice parameters are
thought to be necessary but not sufficient, creating conceptual
framework within which clinicians must apply their training so as
to tailor the evidence to individual patients, most of whom present
with confounding factors unaddressed by clinical trials of
relatively homogeneous patient populations. Accordingly, this
invention embodies an expert-based scoring system--with a logic and
code on the backend--that achieves two critical objectives.
[0099] First, the invention critically evaluates measurable
discrepancies between HCI judgment and expert consensus, the latter
directly addressing gray areas in the evidence base that supports
to varying degrees diagnostic and therapeutic decisions. Second,
the invention tailors the delivery of multi-media educational
programs to narrow the observed educational and performance gaps
until competency is achieved. And once achieved, end users may
progress to subsequent learning modules. Below are comprehensive
reviews of two discrete though related disease states, each
illustrating the nuances of individualized care and the distinct
value of a digital educational environment that--through iterative
trial-and-error decision making--can facilitate advances in
knowledge and evidence based clinical decision making.
Examples of Quantitative and Qualitative Assessment of HCI
Competency
Diabetes
[0100] Type 2 diabetes mellitus (T2DM) is a progressive disease
marked by chronic insulin resistance and inflammation, which drive
disturbances in glucose and lipid metabolism as well as dysfunction
and eventual loss of pancreatic .beta.-cells. Metabolic
abnormalities observed before the onset of frank diabetes are
thought to dampen the insulin sensitivity of skeletal muscle,
contributing to insulin resistance and its sequelae. Central
adiposity is a risk factor for the profound metabolic disturbances
observed in this population. If not treated promptly and
aggressively. T2DM progresses to cerebral, coronary, and peripheral
vasculopathies and peripheral neuropathy. In their efforts to
compensate for insulin resistance, pancreatic .beta.-cells increase
insulin secretion, leading to hyperinsulinemia and eventual
.beta.-cell failure, a hallmark development in patients with T2DM
as well as T1DM.
[0101] In patients with T2DM, hyperglycemia triggers
pathophysiologic processes such as such as vascular oxidative
stress and inflammation, and increased platelet activity, which in
turn lead to microvascular and macrovascular complications.
Moreover, acute postprandial fluctuation in glucose levels is
increasingly recognized as an important pathophysiologic mechanism
alongside basal hyperglycemia. Indeed, studies demonstrate that
postprandial glucose is a stronger predictor of macrovascular
complications that fasting glucose. Absent timely diagnosis and
aggressive treatment, diabetes adversely affects the retina,
nervous system, and kidney, and is the leading cause of blindness
and kidney failure in this country. Additionally, diabetes is
causally related to such macrovascular complications as
cardiovascular disease (CVD), stroke, and peripheral vascular
disease.
[0102] Accumulating evidence indicates that optimal management is
combinatorial, reflecting underlying multifactorial disease
mechanisms. Specific combinations of pharmacologic and
nonpharmacologic interventions are increasingly employed to achieve
predefined treatment goals. Data shows that despite a decline in
CVD events among adults with diabetes (attributed to advances in CV
care), CVD events still occur twice as often in this population as
in adults without diabetes. This finding indicates that trends in
declining risk factors and advances in management of CV events in
patients with diabetes require aggressive educational
activities--as embodied in this invention--that are at once
globally available and individualized to each learner's
demonstrated needs, competencies, and learning style.
[0103] Upon diagnosis, tailoring therapy consistent with patient
needs, goals, and comorbidities remains the threshold challenge in
T2DM. It is noteworthy that guidelines issued jointly by the
American Diabetes Association (ADA) and the European Association
for the Study of Diabetes (EASD) differ significantly from those
issued by the American Association of Clinical Endocrinologists
(AACE) and the American College of Endocrinology (ACE). Such
variability in evidence-based recommendations highlights the
critical importance of clinical judgment and for the proposed
invention. PCPs require an individualized, self-directed
educational experience designed to improve their ability to
formulate patient-specific treatment goals and rational multidrug
regimens.
[0104] The seminal United Kingdom Prospective Diabetes Study
demonstrated that surviving .beta.-cells in patients with diabetes
are ultimately unable to secrete insulin. In fact, by the time of
T2DM diagnosis, independent studies suggest that up to 50% of
.beta.-cell mass has been lost. Critically, these and related
.beta.-cell deficits observed in insulin-resistant and diabetic
patients are potentially reversible, particularly in the early
stages of disease onset. Recognition and treatment of impaired
carbohydrate and lipid metabolism in primary care patients is
therefore critical to blunt the adverse effects of glucotoxicity
and lipotoxicity on the remaining .beta.-cells in T2DM as well as
T1DM.
[0105] Of central importance to optimal diabetic care is
identifying an appropriate level of glycosylated hemoglobin
(A1C)--the preferred glycemic index, reflecting both fasting and
postprandial glucose--and maintaining that level over time through
long-term monitoring and therapeutic adjustments. Importantly, for
every 1% drop in A1C there is a 40% reduction in risk of
microvascular complications. (NIDDK) Recent estimates of the
proportion of patients achieving the ADA A1C goal of <7.0% range
from approximately 50% to 57%, and only 33.0% of patients achieve
the more aggressive AACE A1C goal of <6.5%.
[0106] Similar results have been reported for goals related to
blood pressure (BP<130/80 mm Hg; 45.5%), low-density lipoprotein
cholesterol (LDL-C<100 mg/dL; 45.6%), and an aggregate of A1C
level, BP, and LDL-C (12.2%). The clinical argument for achieving
consensus target values in A1C has been complicated by results from
four recent studies suggesting that tight glycemic control may not
yield clinically meaningful reduction of cardiovascular outcomes
and in select at-risk patients may even cause premature mortality.
These data stand in contrast to results from two other studies,
which showed no significant difference in mortality rates for A1C
goals of .ltoreq.6.5% (ADVANCE*; 11,140 patients; mean duration of
8 years) or <6.0% (VADT*; 1791 patients; mean duration of 11.5
years) versus less intensive goals. Interestingly, the ADVANCE
study demonstrated nephroprotective benefits for patients with near
normal A1C values receiving intensive versus standard glycemic
control.
[0107] Subset analyses of these three trials suggest that there may
in fact be a net cardiovascular benefit of intensive glycemic
control in select patients. Specifically, select patients may
benefit from even lower A1C goals than the general goal of <7%
if this can be achieved without significant hypoglycemia or other
treatment-related adverse effects. Such patients might include
those with short duration of diabetes, long life expectancy, and no
significant cardiovascular disease. Further, in patients with
relatively recent onset diabetes and only modestly elevated A1C,
the contribution of postprandial glucose fluctuations to the
adverse consequences of hyperglycemia is stronger than the
contribution of fasting glucose, indicating a need to restructure
monitoring and treatment strategies accordingly.
[0108] Management of postprandial glucose fluctuations should be a
key component of care along with control of fasting glucose.
[0109] Additional study results have yet to resolve some of the
challenges in goal selection and patient selection. Less stringent
A1C goals than the general goal of <7% have been suggested for
distinct subpopulations. For example, clinicians necessarily
establish realistic goals for special patient populations: those
with a history of severe hypoglycemia, limited life expectancy,
advanced microvascular or macrovascular complications, extensive
comorbid conditions, or long-standing diabetes resistant to
achieving more stringent goals despite multimodal diabetes
therapies.
[0110] Lack of consensus on A1C goal setting is mirrored by the
considerably diverse set of evidence-based practice behaviors. Most
prominently, there are clear differences between the AACE/ACE and
ADA/EASD algorithms for T2DM management. That significant
differences exist reflects the limitations of available evidence
that could otherwise provide answers to questions of fundamental
importance: Which medications are required in newly diagnosed
patients? Which treatments are critical add-ons for patients who
prove only partially responsive? What is the role of insulin in
newly diagnosed, treatment-naive vs treatment-experienced
patients?
[0111] Additional well-designed studies are clearly needed to
resolve these issues and, in particular, to further evaluate the
differential effects of glycemic control in subpopulations.
Meanwhile, clinicians must rely on expert consensus and their own
clinical judgment, again highlighting the distinct benefits of the
invention.
[0112] The limited availability of conclusive evidence underscores
the need for targeted, heuristic education that identified gaps in
knowledge and performance and iteratively delivers multimedia
education until the gaps are bridged and a level of competency--as
established by qualitative and quantitative scoring--is
achieved.
Acute Coronary Syndrome
[0113] Acute coronary syndrome encompasses unstable angina (UA),
non-ST-segment-elevation myocardial infarction (NSTEMI), and
ST-segment-elevation myocardial infarction (STEMI). Despite
significant advances in patient care, significant practice gaps
still remain, all of which are associated with delays in diagnosis
and suboptimal treatment. UA, NSTEMI, and STEMI share a common
pathophysiology involving thrombus formation secondary to either
plaque erosion or rupture.
[0114] Thus, therapy targeting platelet activation/aggregation has
become the standard of care. All patients admitted with ACS receive
aspirin, unless contraindicated. In contemporary practice, patients
also receive a second antiplatelet agent, and the relative risks
and benefits of the available agents for individual patient types
are an intensive area of clinical research.
[0115] Activated platelets release adenosine diphosphate (ADP),
which plays a crucial role in platelet aggregation. There are two
ADP receptors: P2Y1 and P2Y12. Activation of P2Y1 results in only
weak and transient platelet aggregation. In contrast, activation of
P2Y12 is necessary for the full aggregation response to occur.
P2Y12 effects include activation of the glycoprotein (GP) IIb/IIIa
receptor, granule release, amplification of platelet aggregation,
and stabilization of the platelet aggregate. Data from a number of
clinical trials now support the combination of P2Y12 inhibitors
with aspirin.
[0116] Beginning in 2007, studies were reported suggesting that
loss-of-function polymorphism in genes modulating absorption (eg,
ABCB1) and activation (eg, CYP2C19) of clopidogrel (an antiplatelet
agent, thienopyridine, and P2Y12 antagonist) may be associated with
increased risk of thrombotic events. In response to the emerging
data, the FDA in 2009 added a boxed warning to clopidogrel's
prescribing information and alerted clinicians to the availability
of tests for the CYP2C19 loss-of-function allele.
[0117] However, more recently published data suggest that
loss-of-function polymorphisms are only clinically relevant in
clopidogrel-treated ACS patients who undergo stenting and that
these polymorphisms were not associated with increased risk in
clopidogrel-treated ACS patients treated medically.
[0118] Moreover, these polymorphisms are not associated with
increased risk for cardiovascular events in patients treated with
newer generation P2Y12 inhibitors. The clinical data and the FDA
decisions have complicated the management of stabilized patients
just prior to and following discharge from the hospital and
underscore the need for education, particularly on the role of
genetic polymorphism testing.
[0119] A related issue concerns the potential for a diminished
antiplatelet effect resulting from interaction between P2Y12
inhibitors and drugs (such as proton pump inhibitors) that inhibit
cytochrome P450 enzymes. In 2009, observational studies were
reported suggesting that proton pump inhibitors may diminish the
antiplatelet effect of clopidogrel. Again in response to these
data, the FDA issued a public health warning on a possible
interaction between clopidogrel and one specific proton pump
inhibitor, omeprazole. More recently, one study was reported that
supported the FDA decision while another provided contrasting data.
Until this more conclusive evidence becomes available, physicians
will need guidance on deciding whether or not to co-administer a
P2Y12 inhibitor and a proton pump inhibitor.
[0120] Best practice strategies for administering P2Y12 inhibitors
across heterogeneous patient populations with ACS require expert
interpretative insights. Of particular importance to practicing
cardiologists are benefit/risk profiles of currently available
antiplatelet agents, longitudinal assessment and adjustment of
treatment strategies based on patient response and individualizing
therapeutic regimens accordingly. Current guidelines for
antiplatelet therapy were primarily based on multicenter trials
that--like all clinical studies with rigorous inclusion and
exclusion criteria--employed a "one size fits all" dosing strategy.
Emerging treatment paradigms assign utmost importance to tailored
dosing.
[0121] Studies in PCI patients have, for example, linked high
on-treatment platelet reactivity with increased risk for thrombotic
events, supporting the hypothesis that platelet function tests can
improve tailored dosing strategies and reduce cardiovascular risk.
Several tests of platelet function are available, though agreement
among these tests is poor. There is also debate over whether
point-of-care testing of platelet function is needed if genotyping
for CYP2C19 is carried out or if both types of testing should be
conducted. Consequently, experts in cardiology medicine agree that
the role of point-of-care platelet function testing is still
evolving, although the pace of development varies widely.
[0122] Protocols for ensuring accurate dosing of anticoagulants and
parenteral antiplatelet agents and for thienopyridine prescription
at discharge have been included in the American College of
Cardiology/American Heart Association (ACC/AHA) 2008 performance
measures for adults with MI. However, studies demonstrate that many
patients with ACS (approximately one-third to one-half) do not
receive thienopyridine therapy according to the ACC/AHA guidelines.
An analysis of clopidogrel prescriptions found that only 39% met
FDA criteria. The reasons for this practice gap are likely to be
complex, involving physician understanding of agreement with
guidelines.
[0123] Accordingly, to address guideline-based performance gaps
directly as illustrated here, the invention provides a digital,
trial-and-error (heuristic) educational paradigm. As per the latest
in adult learning principles, HCIs will register and select digital
activities of interest, each offering an iterative, needs-based
approach to learning. Each activity begins with a self-assessment
of gaps according to the scoring system discussed previously.
Clinical decision making never occurs in a vacuum. Variability in
physician practice patterns, and the size, location, and type of
hospital all shape the antiplatelet, anticoagulant, and other
pharmacologic therapies employed during the acute phase of an ACS.
Accordingly, the invention utilizes expert consensus--a synthesis
of evidence-based practice and practice-based evidence--as a
yardstick to measure HCI performance.
* * * * *